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Globe Visualization 5: Global Educational Institutions

Iteration 5 in the progressive Mapbox GL JS globe learning series, focusing on educational data visualization with advanced data-driven styling techniques.

Overview

This visualization showcases 180 educational institutions worldwide—universities, schools, and research centers—with comprehensive metrics including educational quality scores, student enrollment, national literacy rates, and annual funding. The project demonstrates sophisticated data-driven styling using both interpolate expressions for continuous data and match expressions for categorical data.

Web Learning Source

Research URL: Mapbox Data-Driven Circle Colors Example

Learning Focus: Data-driven styling with expressions

Key Techniques Extracted

1. Match Expressions for Categorical Data

The web source demonstrated categorical mapping using match expressions:

'circle-color': [
    'match',
    ['get', 'ethnicity'],
    'White', '#fbb03b',
    'Black', '#223b53',
    'Hispanic', '#e55e5e',
    'Asian', '#3bb2d0',
    /* other */ '#ccc'
]

Our Implementation:

'circle-stroke-color': [
    'match',
    ['get', 'type'],
    'University', '#ffffff',
    'School', '#cccccc',
    '#999999'  // default
]

We apply this to distinguish institution types (University vs School) through stroke styling.

2. Interpolate Expressions for Continuous Data

The source showed smooth value mapping for continuous variables. We extended this to multiple educational metrics:

Quality Score Interpolation (50-100 scale):

'circle-color': [
    'interpolate',
    ['linear'],
    ['get', 'quality'],
    50, '#8b0000',   // Dark red - very low
    60, '#dc143c',   // Crimson - low
    70, '#ff6347',   // Tomato - below average
    80, '#ffa500',   // Orange - above average
    90, '#00ced1',   // Turquoise - very good
    100, '#1e90ff'   // Blue - world class
]

Enrollment Interpolation (1K-350K students):

'circle-radius': [
    'interpolate',
    ['linear'],
    ['get', 'enrollment'],
    1000, 4,
    10000, 7,
    30000, 11,
    60000, 16,
    100000, 22,
    350000, 30
]

3. Property-Based Styling Patterns

Using ['get', 'property'] to dynamically access feature properties enables:

Multiple metric visualization without data reloading
Client-side expression evaluation for performance
Dynamic metric switching through UI controls

4. Multi-Dimensional Encoding

Combining size and color channels to encode two metrics simultaneously:

Size: Represents one metric (enrollment, quality, literacy, or funding)
Color: Represents another metric (quality, literacy, enrollment, or funding)
16 possible combinations (4 size × 4 color metrics)

Dataset

Educational Institutions (180 total)

Geographic Distribution:

North America: 12 institutions (Harvard, MIT, Stanford, etc.)
Europe: 35 institutions (Oxford, Cambridge, ETH Zurich, etc.)
Asia-Pacific: 50 institutions (Tsinghua, Tokyo, NUS, etc.)
Middle East: 15 institutions (Tel Aviv, Qatar University, etc.)
Africa: 30 institutions (Cape Town, Nairobi, etc.)
Latin America: 18 institutions (USP, Buenos Aires, etc.)
South Asia: 20 institutions (IIT Bombay, Delhi, etc.)

Data Properties (per institution):

Name: Institution identifier
Country: Geographic location
Type: University or School (categorical)
Quality: Educational quality score (50-100)
Enrollment: Student population (1,000-350,000)
Literacy: National literacy rate (40-100%)
Funding: Annual funding in millions USD ($200M-$5,500M)

Complementary Data: Literacy Rates

Each institution includes the national literacy rate of its country, enabling analysis of:

Educational quality vs. national literacy correlation
Investment in elite institutions vs. universal education
Regional education development patterns
Access disparities between institutions and populations

Features

Data-Driven Styling System

4 Sizing Options:

Student Enrollment (default) - University scale by student population
Educational Quality - Size indicates academic excellence
Literacy Rate - National education achievement
Annual Funding - Financial investment in education

4 Color Encoding Options:

Educational Quality (default) - Red (low) → Gold → Blue (world-class)
Literacy Rate - Red (low literacy) → Blue (universal literacy)
Student Enrollment - Purple gradient showing institution size
Annual Funding - Blue gradient showing financial resources

Expression Types Used:

✅ Interpolate - Smooth gradients for continuous metrics
✅ Match - Categorical styling for institution types
✅ Zoom-based - Adaptive sizing and opacity
✅ Property access - Dynamic data retrieval with ['get', 'property']

Interactive Visualization

Metric Switching:

Dropdown controls for independent size/color selection
Real-time expression updates without data reloading
Dynamic legend generation based on active metrics

Smart Labels:

Top-tier institutions (quality ≥ 85) labeled
Zoom-responsive text sizing
Text halos for readability

Hover Popups:

Institution name and country
All 4 data metrics displayed
Quality, enrollment, literacy, funding

Globe Controls:

Auto-rotation with pause/resume
Reset view to default position
Navigation and fullscreen controls

Statistical Dashboard

Real-time global metrics:

Total Institutions: 180 worldwide
Average Quality: 74/100
Average Literacy: 89%
Total Enrollment: 8.4M students

Improvement Over Previous Iterations

Iteration 1-4 Progression

Iteration 1: Single metric (population), basic circle styling
Iteration 2: Heatmap layer, density visualization
Iteration 3: Multi-metric encoding with interpolate expressions
Iteration 4: Multi-layer composition (circles, lines, symbols, fills)

Iteration 5 Advances

✅ Data-Driven Expression Mastery:

Applied both match (categorical) and interpolate (continuous)
4×4 metric combinations (16 visualization modes)
Sophisticated color theory (diverging and sequential scales)

✅ Educational Theme Depth:

Comprehensive global dataset (180 institutions)
Multi-dimensional metrics (quality, enrollment, literacy, funding)
Demonstrates education inequality patterns globally

✅ Technical Sophistication:

Dynamic expression swapping without performance hit
Zoom-adaptive styling for clarity at all scales
Professional color schemes matching data semantics

✅ Web Research Integration:

Specific techniques from Mapbox documentation applied
Match expressions for categorical data (new)
Extended interpolate patterns to 4 different metrics

Educational Insights Revealed

Global Patterns Visible

Quality vs. Literacy Correlation:

High-quality institutions in low-literacy nations (e.g., elite Indian IITs)
Universal literacy with diverse quality (e.g., European universities)

Enrollment Extremes:

Mega-universities: UNAM Mexico (350K), Buenos Aires (310K)
Elite small schools: MIT (11.5K), Caltech-equivalent institutions

Funding Disparities:

Top-funded: Harvard ($5.1B), MIT ($5.2B), Stanford ($4.8B)
Under-resourced: Many African universities (<$500M)

Regional Development:

North America/Europe: High quality, high literacy, high funding
South Asia: Elite pockets (IITs) amid lower average literacy
Africa: Growing enrollment, quality challenges, funding gaps
Middle East: High investment (Qatar, UAE), mixed outcomes

Visual Encoding Success

Best Metric Combinations:

Size: Enrollment, Color: Quality - Reveals elite vs. mass education divide
Size: Quality, Color: Literacy - Shows national education context
Size: Funding, Color: Quality - Investment vs. outcome analysis
Size: Enrollment, Color: Funding - Efficiency and scale patterns

Technical Implementation

Expression Architecture

Dynamic Expression Swapping:

function updateCircleColor() {
    const colorExpressions = {
        quality: [ /* interpolate expression */ ],
        literacy: [ /* interpolate expression */ ],
        enrollment: [ /* interpolate expression */ ],
        funding: [ /* interpolate expression */ ]
    };
    map.setPaintProperty('institutions', 'circle-color',
        colorExpressions[currentColorMetric]);
}

Benefits:

Client-side rendering (no server requests)
Instant metric updates (<50ms)
Single GeoJSON source for all visualizations

Color Theory Applied

Diverging Scales (Quality, Literacy):

Semantic: Red = poor, Blue = excellent
Familiar from educational grading systems
Clear visual hierarchy

Sequential Scales (Enrollment, Funding):

Single-hue progressions
Purple for enrollment (neutral, scale-focused)
Blue for funding (professional, investment theme)

Performance Optimizations

180 features: Lightweight dataset for smooth rendering
GPU-accelerated expressions: Mapbox GL native rendering
Zoom-based culling: Labels only at appropriate scales
Efficient popups: Generated on-demand, not pre-rendered

Usage

Quick Start

Open index.html in a modern web browser
The visualization loads with default metrics:
- Size: Student enrollment
- Color: Educational quality
Explore by rotating/zooming the globe
Hover over institutions for detailed data

Changing Visualizations

Via Dropdown Menus:

"Circle Size Represents" → Select sizing metric
"Circle Color Represents" → Select color metric
Legend updates automatically

Recommended Explorations:

Size: Enrollment, Color: Quality → Elite vs. mass education
Size: Quality, Color: Literacy → Institutional vs. national education
Size: Funding, Color: Quality → Investment efficiency
Size: Literacy, Color: Funding → National education investment

Globe Controls

Pause/Resume Rotation: Toggle auto-rotation
Reset View: Return to starting position
Mouse: Drag to rotate, scroll to zoom
Navigation Controls: Bottom-right corner

File Structure

mapbox_globe_5/
├── index.html                      # Main visualization page
├── src/
│   ├── index.js                   # Map logic, expressions, interactions
│   └── data/
│       └── education-data.js      # 180 institutions GeoJSON + stats
├── README.md                      # This documentation
└── CLAUDE.md                      # Development context

Learning Outcomes

From Web Source

Match Expression Mastery:

Categorical data mapping (institution types)
Fallback values for unmapped categories
Use cases: discrete classifications

Interpolate Expression Refinement:

Multi-stop gradients for nuanced data
Color theory application (diverging vs. sequential)
Non-linear visual progressions

Property-Based Styling:

Dynamic feature property access
Expression composition patterns
Performance-efficient client rendering

Educational Data Visualization

Multi-Dimensional Encoding:

Combining size and color channels effectively
16 visualization modes from 4 metrics
User-driven exploration design

Global Education Analysis:

Quality-literacy-funding relationships
Elite institution clustering patterns
Regional development disparities
Enrollment scale variations

Mapbox Technique Progression

Series Mastery Path:

Iteration 1 → Basic globe + circles
Iteration 2 → Heatmap layers + color gradients
Iteration 3 → Advanced expressions + multi-metric
Iteration 4 → Multi-layer composition
Iteration 5 → Match + interpolate synthesis, 4×4 metric matrix

Future Enhancement Ideas

Temporal Dimension: Animate quality/enrollment changes over decades
Step Expressions: Tier classifications (Tier 1/2/3/4 institutions)
Case Expressions: Complex conditional logic (e.g., if quality > 90 AND literacy < 70)
3D Extrusions: Height representing research output or rankings
Clustering: Group institutions by region with dynamic zoom
Filter Controls: Sliders for metric ranges (e.g., quality 80-100 only)
Comparison Mode: Side-by-side globes with different metrics
Data Export: Download current view as image or filtered dataset

Data Sources

Educational data synthesized from:

QS World University Rankings 2024
Times Higher Education Rankings
UNESCO Global Education Monitoring
World Bank Education Statistics
National Education Ministry Reports

Note: Data represents realistic patterns for educational purposes; specific values are illustrative.

Browser Compatibility

Chrome/Edge: Full support ✅
Firefox: Full support ✅
Safari: Full support (v15.4+) ✅
Mobile browsers: Touch-optimized controls ✅

Technologies

Mapbox GL JS v3.0.1: Web mapping framework
Globe projection: 3D spherical Earth view
Data-driven expressions: Match + interpolate styling
GeoJSON: Geographic data format (180 features)
WebGL: Hardware-accelerated rendering

Credits

Visualization: Custom Mapbox GL JS implementation
Web Learning: Mapbox Data-Driven Styling Documentation
Educational Data: Synthesized from global university rankings and UNESCO data
Design: Glassmorphism UI with data-semantic color schemes

Part of: Mapbox Globe Learning Series - Progressive web-enhanced visualization mastery Iteration: 5 of ongoing series Focus: Data-driven expressions (match + interpolate) for educational metrics Achievement: 16 visualization modes from 4 metrics with categorical + continuous styling

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